An Exploration of Machine-Learning Estimation of Ground Reaction Force from Wearable Sensor Data.

Danica Hendry,Amity Campbell,Peter O’Sullivan,Catherine Wild,Luke Hopper,Ryan Leadbetter,Kristoffer Mckee,Leon Straker

doi:10.3390/s20030740

Danica Hendry, Amity Campbell + Show 6 more

Open Access

https://doi.org/10.3390/s20030740

Copy DOI

Abstract

This study aimed to develop a wearable sensor system, using machine-learning models, capable of accurately estimating peak ground reaction force (GRF) during ballet jumps in the field. Female dancers (n = 30) performed a series of bilateral and unilateral ballet jumps. Dancers wore six ActiGraph Link wearable sensors (100 Hz). Data were collected simultaneously from two AMTI force platforms and synchronised with the ActiGraph data. Due to sensor hardware malfunctions and synchronisation issues, a multistage approach to model development, using a reduced data set, was taken. Using data from the 14 dancers with complete multi-sensor synchronised data, the best single sensor was determined. Subsequently, the best single sensor model was refined and validated using all available data for that sensor (23 dancers). Root mean square error (RMSE) in body weight (BW) and correlation coefficients (r) were used to assess the GRF profile, and Bland–Altman plots were used to assess model peak GRF accuracy. The model based on sacrum data was the most accurate single sensor model (unilateral landings: RMSE = 0.24 BW, r = 0.95; bilateral landings: RMSE = 0.21 BW, r = 0.98) with the refined model still showing good accuracy (unilateral: RMSE = 0.42 BW, r = 0.80; bilateral: RMSE = 0.39 BW, r = 0.92). Machine-learning models applied to wearable sensor data can provide a field-based system for GRF estimation during ballet jumps.

Highlights

Ground reaction force (GRF) is a commonly measured biomechanical feature during impact-based activities such as landing from a jump [1,2,3,4,5]
The current study demonstrates that the novel application of machine learning to wearable sensor data allows for accurate estimation of peak ground reaction force (GRF) and the GRF profile during dance-specific jumping tasks
Feature extraction testing revealed that a single sensor was capable of predicting

Summary

Introduction

Ground reaction force (GRF) is a commonly measured biomechanical feature during impact-based activities such as landing from a jump [1,2,3,4,5]. Ballet dancers are aesthetic athletes who have been reported to perform up to 220 jumps within a single training session, from over half of which they land unilaterally [6], with peak GRFs commonly exceeding 4 BW [2,7]. High GRF during landings may increase the accumulated internal loads that these athletes experience during training, competition and performance, increasing susceptibility to musculoskeletal pain conditions [3,8]. Recreational athletes have demonstrated 3.4%–6.5% higher peak vertical GRF on landing when fatigued [3]. High peak GRFs during impact-based activities have been associated with

Objectives

Results

Discussion

Conclusion